The role of variable labels in cue probability learning tasks

Two experiments were conducted to investigate effects of variable labels on cue probability learning performance. Variable labels were manipulated to produce conditions in which the actual statistical structure was congruent or incongruent with that implied by the labels. In Experiment 1 the relevant aspect of statistical structure was the relative magnitude of validity of two cues. In Experiment 2, it was the direction of the slope relating the single cue to the criterion. In terms of task knowledge, congruent labels were better than either incongruent or abstract labels in both tasks, suggesting that congruent labels provide useful information about statistical structure. Prediction consistency with congruent labels was higher than with abstract in both experiments, and marginally better than with incongruent labels in Experiment 2. Compared to abstract labels, incongruent labels led to higher consistency in the two-cue task. In the one-cue task, both incongruent labels and neutral labels (which have content but do not imply any particular statistical structure) increased task knowledge. A proposed Rule Selection and Formation model attributes the advantages of variable labels to better retrieval, a reduced set of strategies, and modification of interpretations of labels.